Predicting and assessing the impacts of COVID-19 disruption on marine science and sectors in Australia
Bibliographic record
Abstract
Abstract By March 2020 coronavirus disease 2019 (COVID-19) was anticipated to present a major challenge to the work undertaken by scientists. This pandemic could be considered just one of the shocks that human society has had and will be likely to confront again in the future. As strategic thinking about the future can assist performance and planning of scientific research in the face of change, the pandemic presented an opportunity to evaluate the performance of marine researchers in prediction of future outcomes. In March 2020, two groups of researchers predicted outcomes for the Australian marine research sector, and then evaluated these predictions after 18 months. The self-assessed coping ability of a group experienced in ‘futures studies’ was not higher than the less-experienced group, suggesting that scientists in general may be well placed to cope with shocks. A range of changes to scientific endeavours (e.g., travel, fieldwork) and to marine sectors (e.g., fisheries, biodiversity) were predicted over the first 12–18 months of COVID-19 disruption. The predicted direction of change was generally correct (56%) or neutral (25%) for predictions related to the scientific endeavour, and correct (73%) or mixed (9%) for predictions related to sectors that are the focus of marine research. The success of this foresighting experiment suggests that the collective wisdom of scientists can be used by their organisations to consider the impact of shocks and disruptions and to better prepare for and cope with shocks. Graphical abstract Word cloud analysis of free text responses to questions about expected impact of COVID-19 on the activities associated with marine science
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".